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THE MUTUAL FUNDS OFFERED BY MAJOR SPANISH BANKS GENERATES ALFA? JAVIER CHOLBI DOBLADO |
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In this project we will try to analyse if the "active" mutual funds offered by the major spanish banks generates alpha (obtain better results vs benchmark) in a consistent way along the time, in other words if it's worth to pay highers commisions for buy this mutual funds before to buy ETFs.
Our main objective its to show with data if the active management in the mutual funds industry works properly and try to answer several questions like:
Can the active mutual funds management by the major banks generates alfa?
Its better the performance in the active management vs the pasive management?
We will use our data science powers to generate a stadistical and visual analyse using some machine learning techniques as Linear and Multiple Regression.
Based on definition of our problem, data that will influence in our analysis are:
End of day historical prices of main mutual funds offered by spanish major banks from 2001 until now.
End of day historical prices of main index, for Europe market: EuroStoxx 50 Index.
The source of all the data used in the analysis its provided by Bloomberg across xlsx files.
In this project we will direct our efforts in two principal axis:
Make a deep statistical analysis to show if there are enough evidences to affirm if spanish mutual fund management by major banks are ables to generate alfa across the time and not only in a shor period of time.
Make a deep visualization job using different kind of charts to ilustrate and majke easy to understand our conclusions.
Speaking about statistical techniques we are focus in:
Speaking about visualization we are going to use:
Speaking about Data Science Tecniques we are going to use:
The analysis has 3 parts:
In first place clean the dataset using data wrangling techniques and explore the data using the descriptive statistical.
Second place, draw differents plots to understand better the information and try to see initials answer to resolve our problems.
Last part, use a complex stadistical techniques to make a deep analyisis that allow us to get right conclussions based on data.
We need to transform the dataset into daily returns to analyse the statistical metrics, if we make the analysis directly with the number we are making a big mistake and wasting our time...
In this table we can see the mean, differents percentiles and the standard desviation.
*The frequency histogram are very similar in all the funds except in the RENTA4 fund, this fund has more positive tails than the others distributions and at the same time has a lower VAR ratio...this suppose a important difference and its a very important caracteristic in the active mutual funds...
*The boxplot chart it's very useful to see how is the daily returns behavior. In our analysis we can see that almost all the funds has the same returns with a very similar interquartile range...this its very negative in terms of find active funds...due to his behaviour its the same than the benchmark index...
*All the funds have a very high correlation and R2 ratio, and we can see clearly that the movements in his returns are very similar at the Euro Stoxx 50 daily returns...this give an idea about the active management grade...only the RENTA4 has a ratio lower than 80%.
*Normally for consider a fund as active management performance the Track error ratio needs to be higher than 6%. If we keep this idea in our mind and see the results of our analysis immediately stand out the CAIXABANK and rhe BANKIA funds because his Tracking Error ratio are close to 02...in opposite we have the Renta4 fund with a TE ratio higher than 8% given us a clearly idea about his active performance...
After our analysis we can see the results in the summary table:
BBVA, SABADELL, BANKINTER:
This fund in the 5 year horizon dont seems to generate alfa, normally his returns are very similiar than the benchmark returns even has a worst behaviuor (alfa ratio negative).
In other hand the pearson correlation with the benchmark is so high (higher than 90%) and the R2 ratio is in a similar level.
The Value at risk its almost equal vs Benchmark and happend the same with the return histogram.
The track error is more and less a 6% and the sharpe ratio is normally worst than benchmark.
SANTANDER, CAIXABANK,POPULAR,BANKIA:
This fund in the 5 year horizon seems generate a bit of alfa, normally his returns are very similiar than the benchmark.
In other hand the pearson correlation with the benchmark is so high (higher than 92%) and the R2 ratio is in a similar level.
The Value at risk its almost equal vs Benchmark and happend the same with the return histogram.
The track error is more and less a 8% and the sharpe ratio is normally the same than benchmark.
RENTA4:
This fund in the 5 year horizon seems generate a lot of alfa, always his returns are better than the benchmark.
In other hand the pearson correlation with the benchmark is lower that the rest of the mutual funds high and the R2 ratio is the lowest too.
The Value at risk its almost very different vs Benchmark and happend the same with the return histogram, you can see a very different histogram form...
The track error is sometimes higher than 10% and the sharpe ratio is with difference better vs benchmark (less risk better results).
After analyser all the data and make a deep satatistical and visualization analyst we can conclude the next: